An adaptive trust calibration based autonomous vehicle may include vehicle systems, a system behavior controller, and a driving automation controller. The system behavior controller may generate a driving automation signal indicative of a desired autonomous driving adaptation. The driving automation controller may control the vehicle systems based on parameters including a desired velocity, current velocity of the autonomous vehicle, desired minimum gap distance between the autonomous vehicle and a detected object, current gap distance gap between the autonomous vehicle and a detected object, relative velocity of the detected object with respect to the autonomous vehicle, desired time headway, desired maximum acceleration, desired braking deceleration, and an exponent. The driving automation controller may receive the driving automation signal and implement the desired autonomous driving adaptation via the vehicle systems by adjusting the parameters based on a type of object associated with the detected object.

A support device is a support device by which a support operation to support a host vehicle to stop is performable. The support device includes: a determination unit configured to, when a light color of a traffic light is a light color that requires the host vehicle to stop, determine whether or not alerting on the traffic light is performed as the support operation, based on a speed of the host vehicle and a distance from a current position of the host vehicle to a stop reference position; and a recognition unit configured to recognize a surrounding state around the host vehicle. The stop reference position in a case where a crossing pedestrian is recognized by the recognition unit is closer to the host vehicle than the stop reference position in a case where no pedestrian is recognized by the recognition unit.

A vehicle sharing system includes a vehicle having interior transceiver modules associated with different passenger seating areas and a vehicle computing system (VCS) including a processor and a memory in communication with the modules and programmed to detect occupancy status of each seating area based on signals from the modules and to communicate the occupancy statuses to a remote server to facilitate scheduling of ride-sharing passengers for a specified seating area of the vehicle. The reserved seating location may be used to align the seating location/door with a passenger during pick-up, adjust vehicle accessory settings associated with the reserved seating location, and activate a visual indicator to direct the passenger to the assigned/reserved seating location.

An autonomous driving vehicle includes a user detection monitoring device and a start control device. The user detection monitoring device detects a user who got out of the autonomous driving vehicle after the autonomous driving vehicle stopped at a destination as an alighted user and monitors the alighted user. The start control device maintains a stopped state of the autonomous driving vehicle after the alighted user was detected until a start condition is satisfied and, if the start condition is satisfied, permits a start of the autonomous driving vehicle. The start condition is one of a condition indicating that the alighted user at least moves out of a movement determination area around the autonomous driving vehicle and a condition indicating that the alighted user is present in the movement determination area but remains at the same position for a certain period of time or longer.

An autonomous vehicle computing system can include a primary perception system configured to receive a plurality of sensor data points as input generate primary perception data representing a plurality of classifiable objects and a plurality of paths representing tracked motion of the plurality of classifiable objects. The autonomous vehicle computing system can include a secondary perception system configured to receive the plurality of sensor data points as input, cluster a subset of the plurality of sensor data points of the sensor data to generate one or more sensor data point clusters representing one or more unclassifiable objects that are not classifiable by the primary perception system, and generate secondary path data representing tracked motion of the one or more unclassifiable objects. The autonomous vehicle computing system can generate fused perception data based on the primary perception data and the one or more unclassifiable objects.

There is provided an information processing apparatus, an information processing method, and a program, with which an abnormality of a sensor mounted on a vehicle can be detected. The information processing apparatus includes a first data acquisition unit, a second data acquisition unit, a comparison unit, and a detection unit. The first data acquisition unit acquires first data including at least one of position information of an object present in a vehicle surrounding environment or road surface tilt information and a time stamp, the position information and the road surface tilt information being generated by using sensing data of a sensor mounted on a vehicle. The second data acquisition unit acquires second data including at least one of position information of an object present in a vehicle surrounding environment or road surface tilt information and a time stamp, the position information and the road surface tilt information being generated by using sensing data of a sensor other than that of the vehicle. The comparison unit compares the first data with the second data on the basis of the time stamp included in each of the first data and the second data. The detection unit detects an abnormality related to a sensor mounted on the vehicle on the basis of a comparison result by the comparison unit.

A method for perception fields driving related operations, the method may include (i) obtaining object information regarding one or more objects located within an environment of a vehicle; (ii) determining, using one or more neural network (NNs), one or more virtual forces that are applied on the vehicle, wherein the one or more virtual forces represent one or more impacts of the one or more objects on a behavior of the vehicle; wherein the one or more virtual forces belong to a virtual physical model; and (iii) performing one or more driving related operations of the vehicle based on the one or more virtual forces.

A method of adjusting a driving strategy for a driverless vehicle is provided, which relates to a field of artificial intelligence, in particular to autonomous driving, cloud computing, NLP, computer vision and other fields, and may be applied to an interaction scene between a driverless vehicle and a pedestrian. A specific implementation solution includes: detecting an emotion of at least one pedestrian in response to the at least one pedestrian being detected within a preset range in front of the driverless vehicle; and adjusting a current driving strategy for the driverless vehicle based on a specified emotion in response to detecting that the at least one pedestrian includes a target pedestrian exhibiting the specified emotion.

Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.

A traffic light determining apparatus, a system including the same, and a method thereof, includes: a processor configured to obtain traffic light status information of an intersection while a vehicle is driven, and when obtaining the traffic light status information is not possible, configured to estimate the traffic light status information according to surrounding vehicle information or pedestrian information; and a storage configured to store data and algorithms driven by the processor.